Image blur detection methods and arrangements

ABSTRACT

An apparatus is provided for detecting blur in an image. The apparatus includes an image generator that is configured to generate a plurality of corresponding different resolution images based on a base image. The plurality of corresponding different resolution images is provided to an edge detector. The edge detector detects edge transitions in each of the plurality of corresponding different resolution images and provides edge maps to an edge parameter comparator. The edge parameter comparator compares corresponding edge parameters as detected by the edge detector and provides a result map to a blur calculator. The blur calculator determines at least one blur parameter based on the result map and provides the blur parameter to a blur detector. The blur detector then determines if the base image is blurred based on a comparison of the blur parameter with at least one blur parameter threshold.

TECHNICAL FIELD

[0001] The present invention relates generally to computer imaging, andmore particularly to improved image blur detection methods andarrangements based on edge detection and follow-on comparisoncalculations.

BACKGROUND

[0002] With the increasing popularity of personal computers, handheldappliances and the like, there has been a corresponding increase in thepopularity and affordability of image rendering/manipulationapplications.

[0003] Thus, for example, many personal computers and workstations arebeing configured as multimedia devices that are capable of receivingimage data, for example, directly from a digital camera or indirectlyfrom another networked device. These so-called multimedia devices arefurther configured to display the image data (e.g., still images, video,etc.). As for still images and single video frames, most multimediadevices can be further coupled to a printing device that is configuredto provide a printed hardcopy of the image data.

[0004] When provided with the appropriate software application(s), themultimedia device can be configured to allow the user to manipulate allor portions of the image data in some manner. For example, there is avariety of photo/drawing manipulation applications and video editingapplications available today. One example of a photo/drawingmanipulation program is PhotoDraw® 2000, available from the MicrosoftCorporation of Redmond, Wash. Another example of an image manipulationprogram is Picture It! 2000, also available from the MicrosoftCorporation. One example of a video editing application is AdobePremiere 6.0 available from Adobe Systems Incorporated of San Jose,Calif.

[0005] These and other image manipulation programs provide a multitudeof image editing tools/features. In some instances, for example, in thekey-frame evaluation and photo quality estimation features of PictureIt! 2000, the image manipulation program may need to calculate certaincharacteristics associated with the image data in terms of its'blurriness/sharpness. Doing so allows for the user and/or theapplication to selectively or automatically manipulate blurred imagedata in some desired fashion. For example, a blurred portion of theimage may be sharpened or perhaps protected from additional blurring.

[0006] With this in mind, previous methods for calculating blurcharacteristics have been designed for image restoration. By way ofexample, see the article by M.C. Chiang and T.E. Boult, titled “LocalBlur Estimation and Super-Resolution”, as published in Proc. IEEEComputer Society Conference on Computer Vision and Pattern Recognition,pp. 821-826, June 1997. Also, for example, see the article by R. L.Lagendijk, A. M. Tekalp and J. Biemond, titled “Maximum Likelihood Imageand Blur Identification: A Unifying Approach” as published in OpticalEngineering, 29(5):422-435, May 1990.

[0007] These exemplary conventional techniques utilize methods thatestimate the parameters needed by the reverse process of blur.Unfortunately, these methods tend to be complex and time-consuming.

[0008] Still other techniques utilize compressed domain methods based ondiscrete cosign transform (DCT) coefficient statistics, which can beused to estimate the blurriness of motion picture expert group (MPEG)frame in real-time. For example, see the methods presented by XavierMarichal, Wei-Ying Ma and HongJiang Zhang at the InternationalConference on Image Processing (ICIP) in Kobe, Japan on Oct. 25-29,1999, as published in an article titled “Blur Determination in theCompressed Domain Using DCT Information”. Unfortunately, these methodsoften find it difficult to handle images with relatively large uni-colorpatches.

[0009] Hence, there is an on-going need for new and improved methods forcalculating or otherwise determining blurriness/sharpnesscharacteristics in an image.

SUMMARY

[0010] The present invention provides new and improved methods andarrangements for calculating blurriness/sharpness characteristics in animage. In accordance with certain aspects of the present invention, themethods and arrangements can be provided in a variety of devices orappliances and used to support image rendering/presentation processes,image manipulation processes, and/or other like image data relatedprocesses. In accordance with certain exemplary implementations of thepresent invention, the improved methods and arrangements employ amulti-scale edge amplitude comparison to evaluate the quality of animage, rather than estimating blurriness characteristics as in theconventional methods described above. Furthermore, in certainimplementations, the multi-scale edge amplitude comparison isautomatically adaptable to the image content.

[0011] Thus, for example, in accordance with certain exemplaryimplementations of the present invention, the above stated needs andothers are met by a method that includes detecting edges in a pluralityof corresponding different resolution images, and for each detectededge, comparing corresponding edge parameters associated with thedetected edges in the plurality of corresponding different resolutionimages and determining if the detected edge is blurred. In certainimplementations the edge parameters associated with the detected edgesinclude edge amplitudes.

[0012] The method may also include generating the plurality ofcorresponding different resolution images from a base image, such thatthe resulting plurality of corresponding different resolution imagesincludes the base image and at least one corresponding lower resolutionimage. In certain implementations, for example, the plurality ofcorresponding different resolution images includes the base image asecond corresponding lower resolution image, and a third correspondinglower resolution image that is also lower in resolution than the secondcorresponding lower resolution image.

[0013] In detecting the edges in the plurality of correspondingdifferent resolution images, the method may further include generating acorresponding plurality of detected edge maps. In such a case, the stepof comparing the corresponding edge parameters associated with thedetected edges may also include comparing corresponding edge amplitudesas provided in the plurality of detected edge maps to generate a resultmap.

[0014] The method may include the additional step of calculating a blurparameter based on this result map. For example, the blur parametermight include a blur percentage.

[0015] In still other implementations, the method may also include thestep of determining if the base image is blurred based on a comparisonof the blur parameter with at least one blur parameter threshold.

[0016] In accordance with certain further implementations of the presentinvention, an apparatus is provided. The apparatus includes an edgedetector that is configured to detect edge transitions in a plurality ofcorresponding different resolution images, an edge parameter comparatorthat is configured to compare corresponding edge parameters as detectedby the edge detector, and a blur calculator that is configured todetermine at least one blur parameter based on comparison results asdetermined by the edge parameter comparator. The apparatus may alsoinclude an image generator that is configured to generate the pluralityof corresponding different resolution images based on a base image, andprovide the plurality of corresponding different resolution images tothe edge detector. In still further implementations, the apparatus mayinclude a blur detector that is configured to determine if a base imageis blurred based on a comparison of the at least one blur parameter withat least one blur parameter threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] A more complete understanding of the various methods andarrangements of the present invention may be had by reference to thefollowing detailed description when taken in conjunction with theaccompanying drawings wherein:

[0018]FIG. 1 is a block diagram that depicts an exemplary device, in theform of a computer, which is suitable for use with certainimplementations of the present invention.

[0019]FIGS. 2a-b are line graphs depicting a step edge and a smoothedstep edge, respectively, within exemplary images.

[0020]FIG. 3 is an illustrative representation of a multi-scale imagepyramid having a plurality of different resolutions of the same image,in accordance with certain aspects of the present invention.

[0021]FIG. 4 is a line diagram depicting exemplary correspondingmulti-scale edge amplitudes, in accordance with certain aspects of thepresent invention.

[0022]FIG. 5 is a block diagram associated with an exemplary blurdetector system architecture, in accordance with certain implementationsof the present invention.

[0023]FIG. 6 is a block diagram associated with an exemplary blurdetector algorithm for use in the blur detector system architecture ofFIG. 5, for example, in accordance with certain further implementationsof the present invention.

DETAILED DESCRIPTION

[0024] Turning to the drawings, wherein like reference numerals refer tolike elements, the invention is illustrated as being implemented in asuitable computing environment. Although not required, the inventionwill be described in the general context of computer-executableinstructions, such as program modules, being executed by a personalcomputer. Generally, program modules include routines, programs,objects, components, data structures, etc. that perform particular tasksor implement particular abstract data types. Moreover, those skilled inthe art will appreciate that the invention may be practiced with othercomputer system configurations, including hand-held devices,multi-processor systems, microprocessor based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. The invention may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

[0025]FIG. 1 illustrates an example of a suitable computing environment120 on which the subsequently described methods and arrangements may beimplemented.

[0026] Exemplary computing environment 120 is only one example of asuitable computing environment and is not intended to suggest anylimitation as to the scope of use or functionality of the improvedmethods and arrangements described herein. Neither should computingenvironment 120 be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated incomputing environment 120.

[0027] The improved methods and arrangements herein are operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well known computingsystems, environments, and/or configurations that may be suitableinclude, but are not limited to, personal computers, server computers,thin clients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like.

[0028] As shown in FIG. 1, computing environment 120 includes ageneral-purpose computing device in the form of a computer 130. Thecomponents of computer 130 may include one or more processors orprocessing units 132, a system memory 134, and a bus 136 that couplesvarious system components including system memory 134 to processor 132.

[0029] Bus 136 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus also known as Mezzaninebus.

[0030] Computer 130 typically includes a variety of computer readablemedia. Such media may be any available media that is accessible bycomputer 130, and it includes both volatile and non-volatile media,removable and non-removable media.

[0031] In FIG. 1, system memory 134 includes computer readable media inthe form of volatile memory, such as random access memory (RAM) 140,and/or nonvolatile memory, such as read only memory (ROM) 138. A basicinput/output system (BIOS) 142, containing the basic routines that helpto transfer information between elements within computer 130, such asduring start-up, is stored in ROM 138. RAM 140 typically contains dataand/or program modules that are immediately accessible to and/orpresently being operated on by processor 132.

[0032] Computer 130 may further include other removable/non-removable,volatile/non-volatile computer storage media. For example, FIG. 1illustrates a hard disk drive 144 for reading from and writing to anon-removable, non-volatile magnetic media (not shown and typicallycalled a “hard drive”), a magnetic disk drive 146 for reading from andwriting to a removable, non-volatile magnetic disk 148 (e.g., a “floppydisk”), and an optical disk drive 150 for reading from or writing to aremovable, non-volatile optical disk 152 such as a CD-ROM, CD-R, CD-RW,DVD-ROM, DVD-RAM or other optical media. Hard disk drive 144, magneticdisk drive 146 and optical disk drive 150 are each connected to bus 136by one or more interfaces 154.

[0033] The drives and associated computer-readable media providenonvolatile storage of computer readable instructions, data structures,program modules, and other data for computer 130. Although the exemplaryenvironment described herein employs a hard disk, a removable magneticdisk 148 and a removable optical disk 152, it should be appreciated bythose skilled in the art that other types of computer readable mediawhich can store data that is accessible by a computer, such as magneticcassettes, flash memory cards, digital video disks, random accessmemories (RAMs), read only memories (ROM), and the like, may also beused in the exemplary operating environment.

[0034] A number of program modules may be stored on the hard disk,magnetic disk 148, optical disk 152, ROM 138, or RAM 140, including,e.g., an operating system 158, one or more application programs 160,other program modules 162, and program data 164.

[0035] The improved methods and arrangements described herein may beimplemented within operating system 158, one or more applicationprograms 160, other program modules 162, and/or program data 164.

[0036] A user may provide commands and information into computer 130through input devices such as keyboard 166 and pointing device 168 (suchas a “mouse”). Other input devices (not shown) may include a microphone,joystick, game pad, satellite dish, serial port, scanner, camera, etc.These and other input devices are connected to the processing unit 132through a user input interface 170 that is coupled to bus 136, but maybe connected by other interface and bus structures, such as a parallelport, game port, or a universal serial bus (USB).

[0037] A monitor 172 or other type of display device is also connectedto bus 136 via an interface, such as a video adapter 174. In addition tomonitor 172, personal computers typically include other peripheraloutput devices (not shown), such as speakers and printers, which may beconnected through output peripheral interface 175.

[0038] Computer 130 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer182. Remote computer 182 may include many or all of the elements andfeatures described herein relative to computer 130.

[0039] Logical connections shown in FIG. 1 are a local area network(LAN) 177 and a general wide area network (WAN) 179. Such networkingenvironments are commonplace in offices, enterprise-wide computernetworks, intranets, and the Internet.

[0040] When used in a LAN networking environment, computer 130 isconnected to LAN 177 via network interface or adapter 186. When used ina WAN networking environment, the computer typically includes a modem178 or other means for establishing communications over WAN 179. Modem178, which may be internal or external, may be connected to system bus136 via the user input interface 170 or other appropriate mechanism.

[0041] Depicted in FIG. 1, is a specific implementation of a WAN via theInternet. Here, computer 130 employs modem 178 to establishcommunications with at least one remote computer 182 via the Internet180.

[0042] In a networked environment, program modules depicted relative tocomputer 130, or portions thereof, may be stored in a remote memorystorage device. Thus, e.g., as depicted in FIG. 1, remote applicationprograms 189 may reside on a memory device of remote computer 182. Itwill be appreciated that the network connections shown and described areexemplary and other means of establishing a communications link betweenthe computers may be used.

[0043] This description will now focus on certain aspects of the presentinvention associated with image processing/handling.

[0044] Human vision often relies upon visible edge transitionalinformation to evaluate the quality of an image. For example, whenlooking at an image of a completely white painted smooth wall it wouldbe difficult, if not impossible, for a person to determine if the imageor a portion thereof is blurred. However, if a black line has been drawnacross the surface of the wall, a person would be more likely todetermine if the image or at least the portion containing the black lineis blurred. For example, if the entire image is blurred, than the blackline will appear fuzzy, wider, and/or perhaps gray, etc., as would beexpected for a blurred line/image.

[0045] Recognizing this human ability to detect the blurriness/sharpnessof a line or color/pattern based on the edges, the exemplary methods andarrangements described herein provide a similar technique for devices.

[0046] With this in mind, attention is drawn to FIGS. 2a-b, which areline graphs depicting a step edge and a smoothed step edge,respectively, within exemplary images. These line graphs depict thechanging amplitudes of the image data at a certain points (e.g.,pixels). The step edge, as represented by line 202 in FIG. 2a,illustrates that the amplitude of the image data changes abruptlybetween a first portion of the image (region 204) and a second portionof the image (region 206). This so-called step edge would tend toindicate that the image at regions 204 and 206 is more than likely notblurred, but instead is significantly sharp.

[0047] To the contrary, the smoothed step edge, as represented by line208 in FIG. 2b, illustrates that the amplitude of the image data changesgradually between a first portion of the image (region 210) and a secondportion of the image (region 212). This so-called smoothed step edgewould tend to indicate that the image at regions 210 and 212 is morethan likely blurred, since it is not as sharp a change as the step edgein FIG. 2a.

[0048] Reference is now made to FIG. 3, which is an illustrativerepresentation of a multi-scale image pyramid 300 having a plurality ofdifferent resolutions of the same image, in accordance with certainaspects of the present invention.

[0049] Multi-scale image pyramid 300, as will be described in greaterdetail below, provides a basis for determining if a detected edge withinan image is sufficiently blurred enough to be considered blurred or ifthe detected edge is sufficiently sharp enough to be considered sharp(or not blurred).

[0050] In this example, multi-scale image pyramid 300, includes a baseimage 302 (which may be part of a larger original image 301, forexample) having a resolution of 100×100 pixels, a corresponding secondimage 304 having a reduced resolution of 75×75 pixels, and acorresponding third image 306 having an even more reduced resolution of50×50 pixels. Here, second image 304 and third image 306 have each beengenerated from base image 302 using conventional resolution reductiontechniques.

[0051] While exemplary multi-scale image pyramid 300 includes threelevels of resolution, those skilled in the art will recognize that themethods and arrangements described herein may be implemented with agreater or lesser number of multi-scaled images, as required.

[0052] With this in mind, based on multi-scale image pyramid 300, FIG. 4illustrates the amplitude of a smoothed step edge associated with twodifferent corresponding image resolutions, in accordance with certainaspects of the present invention.

[0053] Here, a differential operator is applied on the smoothed stepedge. As shown, the edge amplitude Δ will change according to the size aof the differential operator. Let σ₁ and Δ₁ be associated with a lowerresolution image in multi-scale image pyramid 300, and σ₂ and Δ₂ beassociated with a higher resolution image in multi-scale image pyramid300. As shown, if σ₁>σ₂, then Δ₁>Δ₂. This property would not exist for asharp edge. Thus, a multi-scale edge amplitude comparison can be used todetect the blurriness/sharpness of images or portions thereof.

[0054] In accordance with certain aspects of the present invention, asdescribed in the exemplary methods and arrangements below, multi-scaledimages are used instead of multi-scale differential operators to reducethe computation complexity.

[0055]FIG. 5 presents a block diagram associated with an exemplary blurdetector system architecture, in accordance with certain implementationsof the present invention.

[0056] Here, an image handling mechanism 500 (e.g., an image renderingand/or manipulation application, or like device/arrangement) includes ablur detector 502 that is configured to receive or otherwise access baseimage 302 (which may be all or part of an original image) and todetermine if base image 302 is “blurred” or “not blurred” according tocertain selectively defined parameters.

[0057]FIG. 6 is a block diagram associated with an exemplary blurdetector algorithm for use in blur detector 502 of FIG. 5, for example,in accordance with certain further implementations of the presentinvention.

[0058] As depicted, blur detector 502 includes a series of functionalblocks that process base image 302 and determine if it is “blurred” or“not blurred”. First, base image 302 is provided to a multi-scale imagegenerator 602, which is configured to generate the images in multi-scaleimage pyramid 300 (FIG. 3). Next, the resulting multi-scale images areprovided to one or more edge operators or detectors, in this example,Sobel edge operators 604 a-b. The edge operators calculate an edgeamplitude on each of the pixels of an image. Pixels having an edgeamplitude greater than a preset threshold are called “edge pixels”. Theedge operators produce corresponding multi-scale edge maps 605, whichare then provided to a multi-scale edge amplitude comparator 606. Aresulting edge amplitude comparison map 607 is then provided to a blurpercentage calculator 608, which produces at least one blurrinessmeasurement, in this example, a blur percentage 609, which is thenprovided to threshold detector 610. Threshold detector 610 determines ifthe blurriness measurement(s) is within or without at least onethreshold range. For example, blur percentage 609 can be compared to adefined, selectively set threshold blur percentage.

[0059] In this manner a comparison of edge amplitudes for variousresolutions of base image 302 is made. For a given detected edge pixelof third image 306, if the edge amplitude is greater than thecorresponding edge amplitude of second image 304, and if the edgeamplitude of second image 304 is greater than the corresponding edgeamplitude of base image 302, then the detected edge pixel is mapped inresult map 607 as “blurred”. This process is completed for all detectededge pixels of third image 306. Blur percentage 609 of base image 302can then be calculated by comparing the number of pixels that are“blurred” in result map 607 with the total number of edge pixels ofthird image 306. Thus, for example, in FIG. 3 if there are 1,000 edgepixels in third image 306, assuming 700 of them have been mapped as“blurred”, then blur percentage 609 would equal 70%. If the thresholdpercentage is set to 65%, then threshold detector 610 would considerbase image 302 as being “blurred”. Conversely, if the thresholdpercentage is set to 75%, then threshold detector 610 would considerbase image 302 as being “not blurred”.

[0060] Moreover, by selectively controlling the size of base image 302,one can further determine if a portion of a larger image, as representedby base image 302, is blurred or not blurred. This may also bedetermined from result map 607. Hence, it may be useful to provideadditional details as to which regions may or may not be determined tobe blurred. Further implementations may allow for additional thresholdvalues, or ranges, that provide additional feedback to the user and/orimage handling mechanism 500.

[0061] Those skilled in the art will recognize that the above-describedexemplary methods and arrangements are adaptable for use with a varietyof color and monochrome image data, including, for example, RGB data,YUV data, CMYK data, etc.

[0062] Although some preferred embodiments of the various methods andarrangements of the present invention have been illustrated in theaccompanying Drawings and described in the foregoing DetailedDescription, it will be understood that the invention is not limited tothe exemplary embodiments disclosed, but is capable of numerousrearrangements, modifications and substitutions without departing fromthe spirit of the invention as set forth and defined by the followingclaims.

What is claimed is:
 1. A method comprising: detecting edges in aplurality of corresponding different resolution images; and for eachdetected edge, comparing corresponding edge parameters associated withthe detected edges in the plurality of corresponding differentresolution images and determining if the detected edge is blurred. 2.The method as recited in claim 1, wherein the corresponding edgeparameters associated with the detected edges in the plurality ofcorresponding different resolution images includes corresponding edgeamplitudes associated with the detected edges in the plurality ofcorresponding different resolution images.
 3. The method as recited inclaim 1, further comprising generating the plurality of correspondingdifferent resolution images from a base image.
 4. The method as recitedin claim 3, wherein the plurality of corresponding different resolutionimages includes the base image and at least one corresponding lowerresolution image.
 5. The method as recited in claim 3, wherein theplurality of corresponding different resolution images includes the baseimage a second corresponding lower resolution image, and a thirdcorresponding lower resolution image that is also lower in resolutionthan the second corresponding lower resolution image.
 6. The method asrecited in claim 3, wherein the base image is a portion of a largeroriginal image.
 7. The method as recited in claim 1, wherein detectingthe edges in the plurality of corresponding different resolution imagesfurther includes generating a corresponding plurality of detected edgemaps.
 8. The method as recited in claim 7, wherein comparing thecorresponding edge parameters associated with the detected edges in theplurality of corresponding different resolution images further includescomparing corresponding edge amplitudes as provided in the plurality ofdetected edge maps to generate a result map.
 9. The method as recited inclaim 8, further comprising calculating a blur parameter based on theresult map.
 10. The method as recited in claim 9, wherein the blurparameter includes a blur percentage parameter.
 11. The method asrecited in claim 9, further comprising generating the plurality ofcorresponding different resolution images from a base image anddetermining if the base image is blurred based on a comparison of theblur parameter with at least one blur parameter threshold.
 12. Acomputer-readable medium having computer-implementable instructions forperforming acts comprising: locating edges in a plurality ofcorresponding different resolution samples of an image; and for eachlocated edge, comparing corresponding edge parameters associated withthe located edges in the plurality of corresponding different resolutionsamples to determine if the located edge is significantly blurred. 13.The computer-readable medium as recited in claim 12, wherein thecorresponding edge parameters associated with the located edges in theplurality of corresponding different resolution samples includeamplitudes associated with located edges.
 14. The computer-readablemedium as recited in claim 12, having further computer-implementableinstructions for performing acts comprising: providing a base image filehaving image data associated with the image; and processing the baseimage to produce one or more corresponding different resolution samplesof the image.
 15. The computer-readable medium as recited in claim 14,wherein the plurality of corresponding different resolution samplesincludes the base image and at least one corresponding lower resolutionsample.
 16. The computer-readable medium as recited in claim 14, whereinthe plurality of corresponding different resolution samples includes thebase image, a second corresponding lower resolution sample, and a thirdcorresponding lower resolution sample that is also lower in resolutionthan the second corresponding lower resolution sample.
 17. Thecomputer-readable medium as recited in claim 14, wherein the base imageis a portion of a larger original image.
 18. The computer-readablemedium as recited in claim 12, wherein locating the edges in theplurality of corresponding different resolution samples further includesgenerating a corresponding plurality of detected edge maps.
 19. Thecomputer-readable medium as recited in claim 18, wherein comparing thecorresponding edge parameters associated with the located edges in theplurality of corresponding different resolution samples further includescomparing amplitudes as provided in the plurality of detected edge mapsto generate a result map.
 20. The computer-readable medium as recited inclaim 19, having further computer-implementable instructions forperforming acts comprising calculating at least one blur parameter basedon the result map.
 21. The computer-readable medium as recited in claim20, wherein the blur parameter includes a blur percentage.
 22. Thecomputer-readable medium as recited in claim 20, having furthercomputer-implementable instructions for performing acts comprising:creating the plurality of corresponding different resolution samplesfrom a base image; and determining if selected portions of the baseimage are blurred based on a comparison of the blur parameter with atleast one threshold value.
 23. An apparatus comprising: an edge detectorconfigured to detect edge transitions in a plurality of correspondingdifferent resolution images; an edge parameter comparator configured tocompare corresponding edge parameters as detected by the edge detector;and a blur calculator configured to determine at least one blurparameter based on comparison results as determined by the edgeparameter comparator.
 24. The apparatus as recited in claim 23, whereinthe corresponding edge parameters include corresponding edge amplitudesassociated with the detected edge transitions.
 25. The apparatus asrecited in claim 23, further comprising an image generator configured togenerate the plurality of corresponding different resolution imagesbased on an base image, and provide the plurality of correspondingdifferent resolution images to the edge detector.
 26. The apparatus asrecited in claim 25, wherein the plurality of corresponding differentresolution images includes the base image and at least one correspondinglower resolution image.
 27. The apparatus as recited in claim 25,wherein the plurality of corresponding different resolution imagesincludes the base image a second corresponding lower resolution image,and a third corresponding lower resolution image that is also lower inresolution than the second corresponding lower resolution image.
 28. Theapparatus as recited in claim 25, wherein the base image is a portion ofa larger original image.
 29. The apparatus as recited in claim 23,wherein the edge detector is further configured to generate acorresponding plurality of detected edge maps.
 30. The apparatus asrecited in claim 29, wherein the edge parameter comparator is furtherconfigured to compare corresponding edge amplitudes as provided in theplurality of detected edge maps to generate a result map.
 31. Theapparatus as recited in claim 30, wherein the blur calculator is furtherconfigured to calculate the at least one blur parameter based on theresult map.
 32. The apparatus as recited in claim 31, wherein the atleast one blur parameter includes a blur percentage parameter.
 33. Theapparatus as recited in claim 25, further comprising a blur detectorconfigured to determine if the base image is blurred based on acomparison of the at least one blur parameter with at least one blurparameter threshold.
 34. The apparatus as recited in claim 23, whereinthe apparatus is configured within logic and memory of a computer.